Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix)
Autoři:
Ryan S. Senger aff001; Meaghan Sullivan aff001; Austin Gouldin aff001; Stephanie Lundgren aff001; Kristen Merrifield aff001; Caitlin Steen aff001; Emily Baker aff001; Tommy Vu aff002; Ben Agnor aff001; Gabrielle Martinez aff001; Hana Coogan aff001; William Carswell aff001; Varun Kavuru aff004; Lampros Karageorge aff004; Devasmita Dev aff004; Pang Du aff005; Allan Sklar aff006; James Pirkle, Jr aff007; Susan Guelich aff008; Giuseppe Orlando aff009; John L. Robertson aff003
Působiště autorů:
Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
aff001; Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
aff002; DialySenors, Inc., Blacksburg, Virginia, United States of America
aff003; Veteran Affairs Medical Center, Salem, Virginia, United States of America
aff004; Department of Statistics, Virginia Tech, Blacksburg, Virginia, United States of America
aff005; Lewis-Gale Medical Center, Salem, Virginia, United States of America
aff006; Department of Internal Medicine–Nephrology, Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina, United States of America
aff007; Valley Nephrology Associates, Roanoke, Virginia, United States of America
aff008; Department of Surgical Sciences–Transplant, Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina, United States of America
aff009; Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, United States of America
aff010; Virginia Tech-Carilion School of Medicine and Research Institute, Blacksburg, Virginia, United States of America
aff011
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227281
Souhrn
Raman Chemometric Urinalysis (RametrixTM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. RametrixTM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing “unknown” specimens, RametrixTM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. RametrixTM was able to identify “unknown” urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.
Klíčová slova:
Biomarkers – Chronic kidney disease – Medical dialysis – Metabolomics – principal component analysis – Specimen preparation and treatment – Statistical data – Urine
Zdroje
1. Echeverry G, Hortin GL, Rai AJ. Introduction to urinalysis: historical perspectives and clinical application. Methods Mol Biol Clifton NJ. 2010;641: 1–12. doi: 10.1007/978-1-60761-711-2_1 20407938
2. Laposata M, editor. Laboratory Medicine: The diagnosis of disease in the clinical laboratory. 2nd ed. New York: McGraw-Hill Education; 2014.
3. Levey AS, Becker C, Inker LA. Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: a systematic review. JAMA. 2015;313: 837–846. doi: 10.1001/jama.2015.0602 25710660
4. Mundt L, Shanahan K. Graff’s textbook of urinalysis and body fluids. 3rd ed. Philadelphia, PA: Wolters-Klower; 2016.
5. Fisher AK, Carswell WF, Athamneh AIM, Sullivan MC, Robertson JL, Bevan DR, et al. The RametrixTM LITE Toolbox v1.0 for MATLAB®. J Raman Spectrosc. 2018;49: 885–896. doi: 10.1002/jrs.5348
6. Senger R, Robertson J. The RametrixTM PRO Toolbox v1.0 for MATLAB®. In Press. 2019
7. Senger R, Du P, DeLaTorre Campos D, Carswell W, Webster K, Sullivan M, et al. Assessment of urine specimen storage conditions using Raman Chemometric UrinalysisTM (RametrixTM). Under Review. 2019
8. Senger R, Kavuru V, Sullivan M, Gouldin A, Lundgren S, Merrifield K, et al. Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix). PLOS ONE 2019; doi: 10.1371/journal.pone.0222115 31560690
9. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, et al. The Human Urine Metabolome. PLOS ONE. 2013;8: e73076. doi: 10.1371/journal.pone.0073076 24023812
10. Movasaghi Z, Rehman S, Rehman DIU. Raman Spectroscopy of Biological Tissues. Appl Spectrosc Rev. 2007;42: 493–541. doi: 10.1080/05704920701551530
11. Pence I, Mahadevan-Jansen A. Clinical instrumentation and applications of Raman spectroscopy. Chem Soc Rev. 2016;45: 1958–1979. doi: 10.1039/c5cs00581g 26999370
12. Gabler J, Wang S. Quantification of metanephrine and normetanephrine in urine using liquid chromatography-tandem mass spectrometry. Methods Mol Biol Clifton NJ. 2016;1378: 149–157. doi: 10.1007/978-1-4939-3182-8_17
13. Lenders JWM, Williams TA, Reincke M, Gomez-Sanchez CE. DIAGNOSIS OF ENDOCRINE DISEASE: 18-Oxocortisol and 18-hydroxycortisol: is there clinical utility of these steroids? Eur J Endocrinol. 2018;178: R1–R9. doi: 10.1530/EJE-17-0563 28904009
14. Willrich MAV, Katzmann JA. Laboratory testing requirements for diagnosis and follow-up of multiple myeloma and related plasma cell dyscrasias. Clin Chem Lab Med. 2016;54: 907–919. doi: 10.1515/cclm-2015-0580 26509779
15. Bax C, Taverna G, Eusebio L, Sironi S, Grizzi F, Guazzoni G, et al. Innovative diagnostic methods for early prostate cancer detection through urine analysis: A review. Cancers. 2018;10. doi: 10.3390/cancers10040123 29670060
16. Alves RC, Fernandes RP, Eloy JO, Salgado HRN, Chorilli M. Characteristics, Properties and analytical methods of Paclitaxel: A review. Crit Rev Anal Chem. 2018;48: 110–118. doi: 10.1080/10408347.2017.1416283 29239659
17. Mahajan G. Role of Urine Drug Testing in the Current Opioid Epidemic. Anesth Analg. 2017;125: 2094–2104. doi: 10.1213/ANE.0000000000002565 29189366
18. Richter P, Faroon O, Pappas RS. Cadmium and cadmium/zinc ratios and Tobacco-Related morbidities. Int J Environ Res Public Health. 2017;14. doi: 10.3390/ijerph14101154 28961214
19. Zürbig P, Mischak H. Capillary electrophoresis coupled to mass spectrometry for biomarker discovery and diagnosis of kidney diseases. Contrib Nephrol. 2008;160: 107–126. doi: 10.1159/000125938 18401165
20. Reichel RR. Acute kidney injury: quoi de neuf? Ochsner J. 2014;14: 359–368. 25249802
21. Prakoura N, Chatziantoniou C. Periostin in kidney diseases. Cell Mol Life Sci CMLS. 2017;74: 4315–4320. doi: 10.1007/s00018-017-2650-6 28884334
22. Shardlow A, McIntyre NJ, Fraser SDS, Roderick P, Raftery J, Fluck RJ, et al. The clinical utility and cost impact of cystatin C measurement in the diagnosis and management of chronic kidney disease: A primary care cohort study. PLoS Med. 2017;14: e1002400. doi: 10.1371/journal.pmed.1002400 29016597
23. Edelstein C. Biomarkers of acute kidney injury. Biomarkers of kidney disease. 2nd ed. Academic Press; 2017.
24. Athamneh AIM, Alajlouni RA, Wallace RS, Seleem MN, Senger RS. Phenotypic Profiling of Antibiotic Response Signatures in Escherichia coli Using Raman Spectroscopy. Antimicrob Agents Chemother. 2014;58: 1302–1314. doi: 10.1128/AAC.02098-13 24295982
25. Freedman BG, Zu TNK, Wallace RS, Senger RS. Raman spectroscopy detects phenotypic differences among Escherichia coli enriched for 1-butanol tolerance using a metagenomic DNA library. Biotechnol J. 2016;11: 877–889. doi: 10.1002/biot.201500144 26814030
26. Zu TNK, Athamneh AIM, Collakova E, Robertson J, Hawken T, Aardema C, et al. Assessment of ex vivo perfused liver health by Raman spectroscopy. J Raman Spectrosc. 2015;46: 551–558. doi: 10.1002/jrs.4688
27. Zu TNK, Athamneh AIM, Wallace RS, Collakova E, Senger RS. Near-Real-Time Analysis of the Phenotypic Responses of Escherichia coli to 1-Butanol Exposure Using Raman Spectroscopy. J Bacteriol. 2014;196: 3983–3991. doi: 10.1128/JB.01590-14 25157078
28. Liu J, Sun J, Huang X, Li G, Liu B. Goldindec: A novel algorithm for Raman spectrum baseline correction. Appl Spectrosc. 2015;69: 834–842. doi: 10.1366/14-07798 26037638
29. Premasiri WR, Clarke RH, Womble ME. Urine analysis by laser Raman spectroscopy. Lasers Surg Med. 2001;28: 330–334. doi: 10.1002/lsm.1058 11344513
30. Frank CJ, McCreery RL, Redd DCB. Raman Spectroscopy of normal and diseased human breast tissues. Anal Chem. 1995;67: 777–783. doi: 10.1021/ac00101a001 7762814
31. Gniadecka M, Wulf HC, Mortensen NN, Nielsen OF, Christensen DH. Diagnosis of Basal cell carcinoma by Raman spectroscopy. J Raman Spectrosc. 1997;28: 125–129. doi: 10.1002/(SICI)1097-4555(199702)28:2/3<125::AID-JRS65>3.0.CO;2-#
32. Krafft C, Neudert L, Simat T, Salzer R. Near infrared Raman spectra of human brain lipids. Spectrochim Acta A Mol Biomol Spectrosc. 2005;61: 1529–1535. doi: 10.1016/j.saa.2004.11.017 15820887
Článek vyšel v časopise
PLOS One
2020 Číslo 1
- Proč se v Česku šíří několik „vymýcených“ infekcí najednou? Ptáme se odborníků
- ADHD jako evoluční výhoda? Výzkum se zaměřil na lovce a sběrače
- Není migréna jako migréna: Co pociťují ženy a co muži?
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Není statin jako statin aneb praktický přehled rozdílů jednotlivých molekul
Nejčtenější v tomto čísle
- Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger sounds
- Chemical analysis of snus products from the United States and northern Europe
- Calcium dobesilate reduces VEGF signaling by interfering with heparan sulfate binding site and protects from vascular complications in diabetic mice
- Effect of Lactobacillus acidophilus D2/CSL (CECT 4529) supplementation in drinking water on chicken crop and caeca microbiome
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy